What does "normal" Twitter traffic look like in terms of what percentage of it is automated/from accounts with default pics/new accounts etc? It turns out that the baseline values differ depending on tweet language.

cc: @ZellaQuixote
We downloaded 100K random tweets in each of 9 languages: Arabic, Chinese, English, Japanese, Korean, Portuguese, Russian, Spanish, and Turkish. (We did this by choosing 1000 random cutoff times between Feb 8 and Feb 16, and downloading the preceding 100 tweets in each language.)
The percentage of tweets sent via automation apps (based on the "tweet source" field) varies widely by language. Tweets in Japanese have the highest rate of automation (16.9%) of tweets, with Arabic on the low end at only 0.9%. 5.9% of English-language tweets are automated.
For all nine languages, the most common app is one of the official Twitter smartphone apps, but whether Android or iPhone is dominant varies. Japanese and Korean are the only languages where the automation app twittbot accounts for at least 5% of the traffic.
For all nine languages, the most common app is one of the official Twitter smartphone apps, but whether Android or iPhone is dominant varies. Japanese and Korean are the only languages where the automation app twittbot accounts for at least 5% of the traffic.
What percentage of "normal" Twitter traffic comes from accounts with default profile pics? It again varies by language: they show up more than twice as frequently in Chinese tweets (4.7% sent by accounts with default pics) than any other language we checked.
Finally, the proportion of tweets handled by each of Twitter's datacenters varies from language to language. (Datacenter info is encoded in tweet IDs, along with a few other things - more info in the linked blog post.)
blog.twitter.com/engineering/en…

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More from @conspirator0

19 Feb
Meet @DaoThuyHanh1, @TrangKi26074705, @NguyenN16424388, and @TrinhNg82469771, four automated Twitter accounts that were created this morning (Feb 18 2021) and are already spewing exciting questions like "Do People Actually Use Cryptocurrency?" into the universe.

cc @ZellaQuixote Image
These accounts are part of a botnet consisting of 34 accounts created in Feb 2021. They all follow multiple other members of the botnet (and not much else). Their follow graph is split into two separate clusters, with bots in each cluster only following others in that cluster. ImageImageImageImage
The majority of this network's content thus far is tweets containing linkings to cryptocurrency news articles and blog posts. These tweets are sent via the SocialChief automation service, which we've seen before:
Image
Read 5 tweets
16 Feb
It's a great day to look at an Arabic-language pornbot network that uses stolen profile pics. This particular botnet sometimes uses the same pic on multiple accounts, occasionally cropped differently. #MondaySpam

cc: @ZellaQuixote
This network consists of 303 accounts created from November 2020 to January 2021, with particularly large batches created on November 13th, November 15th, and December 16th, 2020.
Most of this botnet's content is in Arabic, most of it is retweets, and most of it is (allegedly) sent via the Twitter Web App. The retweets were all sent via the web app, and its original content was posted via Twitter for Advertisers, Twitter Ads, and Tweetdeck.
Read 5 tweets
15 Feb
How does one detect renamed Twitter accounts and find the previous names? There's no surefire way to do it, but here are four methods that sometimes work.

cc: @ZellaQuixote
The first method is to do a Twitter search for old replies to the account in question. Use a search of this form to find replies prior to a given date and make sure to use "Latest" rather than "Top" results.

to:FMAR122 OR @FMAR122 until:2014-01-01
The previous name(s) will show up (sometimes alongside the current name) at the beginning of replies to the account's old tweets. This method doesn't always work, but it seems that when it does work, it works even if the tweets being replied to have been deleted.
Read 11 tweets
14 Feb
Meet @Dilde97512368, a Twitter account with a GAN-generated profile pic that is followed by lots of other accounts with GAN-generated pics.

(GAN = "generative adversarial network", the AI technique used by thispersondoesnotexist.com to generate fake face pics.)

cc: @ZellaQuixote
60 of @Dilde97512368's followers have GAN-generated face pics, but that's not the only pattern. 15 of its followers use cat pics, 7 use anime pics, and some of the pics that are neither GAN faces, cats, nor anime pics are repeated across two or three of its followers.
We explored the follower networks of @Dilde97512368's followers, and found a total of 3204 accounts with the same mix of profile pics (anime, cats, GAN-generated faces, and repeated images), all created January 25th or later.
Read 14 tweets
12 Feb
Why does Venezuelan government account @Mippcivzla get so many more retweets than likes on its tweets? #AstroturfFriday

cc: @ZellaQuixote
Answer: @Mippcivzla's tweets (and those of a few other large Venezuelan accounts) are being retweeted by a network of 2454 accounts, all allegedly using the Twitter Android app. These accounts post almost no original content (99.6% percent of their tweets are retweets).
These accounts have all retweeted hundreds of tweets or more, but have liked few or none. The content they boost reflects this - 98.6% of the tweets they retweeted got more retweets than likes. (Based on a set of 5M random tweets, only ~0.8% of tweets get more RTs than likes.)
Read 6 tweets
10 Feb
The 9 accounts promoting monsterfundrise(dot)com discussed in this previous thread have been shut down by Twitter, but 15 new ones have taken their place. As before, their tweets appear be being astroturfed, garnering far more retweets than likes.

cc: @ZellaQuixote
We downloaded the set of accounts amplifying the monsterfundrise tweets, and noticed that many of the other tweets they retweeted (particularly recent tweets from Punjab, Pakistan governor @ChMSarwar) also received more retweets than likes.
(some background info on the presence of more retweets than likes being a sign of astroturfing - average ratio is more than twice as many likes as retweets)


Read 11 tweets

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